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# ==========================================================================

# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================

# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os
import os.path as osp
from collections import OrderedDict
from typing import Dict, Optional, Union

import torch
import torch.distributed as dist

import mmcv
from mmcv.fileio.file_client import FileClient
from mmcv.utils import is_tuple_of, scandir
from ..hook import HOOKS
from .base import LoggerHook


@HOOKS.register_module()
class TextLoggerHook(LoggerHook):
    """Logger hook in text.

    In this logger hook, the information will be printed on terminal and
    saved in json file.

    Args:
        by_epoch (bool, optional): Whether EpochBasedRunner is used.
            Default: True.
        interval (int, optional): Logging interval (every k iterations).
            Default: 10.
        ignore_last (bool, optional): Ignore the log of last iterations in each
            epoch if less than :attr:`interval`. Default: True.
        reset_flag (bool, optional): Whether to clear the output buffer after
            logging. Default: False.
        interval_exp_name (int, optional): Logging interval for experiment
            name. This feature is to help users conveniently get the experiment
            information from screen or log file. Default: 1000.
        out_dir (str, optional): Logs are saved in ``runner.work_dir`` default.
            If ``out_dir`` is specified, logs will be copied to a new directory
            which is the concatenation of ``out_dir`` and the last level
            directory of ``runner.work_dir``. Default: None.
            `New in version 1.3.16.`
        out_suffix (str or tuple[str], optional): Those filenames ending with
            ``out_suffix`` will be copied to ``out_dir``.
            Default: ('.log.json', '.log', '.py').
            `New in version 1.3.16.`
        keep_local (bool, optional): Whether to keep local log when
            :attr:`out_dir` is specified. If False, the local log will be
            removed. Default: True.
            `New in version 1.3.16.`
        file_client_args (dict, optional): Arguments to instantiate a
            FileClient. See :class:`mmcv.fileio.FileClient` for details.
            Default: None.
            `New in version 1.3.16.`
    """

    def __init__(self,
                 by_epoch: bool = True,
                 interval: int = 10,
                 ignore_last: bool = True,
                 reset_flag: bool = False,
                 interval_exp_name: int = 1000,
                 out_dir: Optional[str] = None,
                 out_suffix: Union[str, tuple] = ('.log.json', '.log', '.py'),
                 keep_local: bool = True,
                 file_client_args: Optional[Dict] = None):
        super().__init__(interval, ignore_last, reset_flag, by_epoch)
        self.by_epoch = by_epoch
        self.time_sec_tot = 0
        self.interval_exp_name = interval_exp_name

        if out_dir is None and file_client_args is not None:
            raise ValueError(
                'file_client_args should be "None" when `out_dir` is not'
                'specified.')
        self.out_dir = out_dir

        if not (out_dir is None or isinstance(out_dir, str)
                or is_tuple_of(out_dir, str)):
            raise TypeError('out_dir should be  "None" or string or tuple of '
                            'string, but got {out_dir}')
        self.out_suffix = out_suffix

        self.keep_local = keep_local
        self.file_client_args = file_client_args
        if self.out_dir is not None:
            self.file_client = FileClient.infer_client(file_client_args,
                                                       self.out_dir)

    def before_run(self, runner) -> None:
        super().before_run(runner)

        if self.out_dir is not None:
            self.file_client = FileClient.infer_client(self.file_client_args,
                                                       self.out_dir)
            # The final `self.out_dir` is the concatenation of `self.out_dir`
            # and the last level directory of `runner.work_dir`
            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
            self.out_dir = self.file_client.join_path(self.out_dir, basename)
            runner.logger.info(
                f'Text logs will be saved to {self.out_dir} by '
                f'{self.file_client.name} after the training process.')

        self.start_iter = runner.iter
        self.json_log_path = osp.join(runner.work_dir,
                                      f'{runner.timestamp}.log.json')
        if runner.meta is not None:
            self._dump_log(runner.meta, runner)

    def _get_max_memory(self, runner) -> int:
        device = getattr(runner.model, 'output_device', None)
        mem = torch.cuda.max_memory_allocated(device=device)
        mem_mb = torch.tensor([int(mem) // (1024 * 1024)],
                              dtype=torch.int,
                              device=device)
        if runner.world_size > 1:
            dist.reduce(mem_mb, 0, op=dist.ReduceOp.MAX)
        return mem_mb.item()

    def _log_info(self, log_dict: Dict, runner) -> None:
        # print exp name for users to distinguish experiments
        # at every ``interval_exp_name`` iterations and the end of each epoch
        if runner.meta is not None and 'exp_name' in runner.meta:
            if (self.every_n_iters(runner, self.interval_exp_name)) or (
                    self.by_epoch and self.end_of_epoch(runner)):
                exp_info = f'Exp name: {runner.meta["exp_name"]}'
                runner.logger.info(exp_info)

        if log_dict['mode'] == 'train':
            if isinstance(log_dict['lr'], dict):
                lr_str = []
                for k, val in log_dict['lr'].items():
                    lr_str.append(f'lr_{k}: {val:.3e}')
                lr_str = ' '.join(lr_str)  # type: ignore
            else:
                lr_str = f'lr: {log_dict["lr"]:.3e}'  # type: ignore

            # by epoch: Epoch [4][100/1000]
            # by iter:  Iter [100/100000]
            if self.by_epoch:
                log_str = f'Epoch [{log_dict["epoch"]}]' \
                          f'[{log_dict["iter"]}/{len(runner.data_loader)}]\t'
            else:
                log_str = f'Iter [{log_dict["iter"]}/{runner.max_iters}]\t'
            log_str += f'{lr_str}, '

            if 'time' in log_dict.keys():
                self.time_sec_tot += (log_dict['time'] * self.interval)
                time_sec_avg = self.time_sec_tot / (
                    runner.iter - self.start_iter + 1)
                eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1)
                eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
                log_str += f'eta: {eta_str}, '
                log_str += f'time: {log_dict["time"]:.3f}, ' \
                           f'data_time: {log_dict["data_time"]:.3f}, '
                # statistic memory
                if torch.cuda.is_available():
                    log_str += f'memory: {log_dict["memory"]}, '
        else:
            # val/test time
            # here 1000 is the length of the val dataloader
            # by epoch: Epoch[val] [4][1000]
            # by iter: Iter[val] [1000]
            if self.by_epoch:
                log_str = f'Epoch({log_dict["mode"]}) ' \
                    f'[{log_dict["epoch"]}][{log_dict["iter"]}]\t'
            else:
                log_str = f'Iter({log_dict["mode"]}) [{log_dict["iter"]}]\t'

        log_items = []
        for name, val in log_dict.items():
            # TODO: resolve this hack
            # these items have been in log_str
            if name in [
                    'mode', 'Epoch', 'iter', 'lr', 'time', 'data_time',
                    'memory', 'epoch'
            ]:
                continue
            if isinstance(val, float):
                val = f'{val:.4f}'
            log_items.append(f'{name}: {val}')
        log_str += ', '.join(log_items)

        runner.logger.info(log_str)

    def _dump_log(self, log_dict: Dict, runner) -> None:
        # dump log in json format
        json_log = OrderedDict()
        for k, v in log_dict.items():
            json_log[k] = self._round_float(v)
        # only append log at last line
        if runner.rank == 0:
            with open(self.json_log_path, 'a+') as f:
                mmcv.dump(json_log, f, file_format='json')
                f.write('\n')

    def _round_float(self, items):
        if isinstance(items, list):
            return [self._round_float(item) for item in items]
        elif isinstance(items, float):
            return round(items, 5)
        else:
            return items

    def log(self, runner) -> OrderedDict:
        if 'eval_iter_num' in runner.log_buffer.output:
            # this doesn't modify runner.iter and is regardless of by_epoch
            cur_iter = runner.log_buffer.output.pop('eval_iter_num')
        else:
            cur_iter = self.get_iter(runner, inner_iter=True)

        log_dict = OrderedDict(
            mode=self.get_mode(runner),
            epoch=self.get_epoch(runner),
            iter=cur_iter)

        # only record lr of the first param group
        cur_lr = runner.current_lr()
        if isinstance(cur_lr, list):
            log_dict['lr'] = cur_lr[0]
        else:
            assert isinstance(cur_lr, dict)
            log_dict['lr'] = {}
            for k, lr_ in cur_lr.items():
                assert isinstance(lr_, list)
                log_dict['lr'].update({k: lr_[0]})

        if 'time' in runner.log_buffer.output:
            # statistic memory
            if torch.cuda.is_available():
                log_dict['memory'] = self._get_max_memory(runner)

        log_dict = dict(log_dict, **runner.log_buffer.output)  # type: ignore

        self._log_info(log_dict, runner)
        self._dump_log(log_dict, runner)
        return log_dict

    def after_run(self, runner) -> None:
        # copy or upload logs to self.out_dir
        if self.out_dir is not None:
            for filename in scandir(runner.work_dir, self.out_suffix, True):
                local_filepath = osp.join(runner.work_dir, filename)
                out_filepath = self.file_client.join_path(
                    self.out_dir, filename)
                with open(local_filepath) as f:
                    self.file_client.put_text(f.read(), out_filepath)

                runner.logger.info(
                    f'The file {local_filepath} has been uploaded to '
                    f'{out_filepath}.')

                if not self.keep_local:
                    os.remove(local_filepath)
                    runner.logger.info(
                        f'{local_filepath} was removed due to the '
                        '`self.keep_local=False`')
